class_docstr: Distribution class for the Implicit Quantile from which
  we can sample or calculate the quantile loss.
  
  Parameters
  ----------
  outputs
      Outputs from the Implicit Quantile Network.
  taus
      Tensor random numbers from the Beta or Uniform distribution for the
      corresponding outputs.
code: def sample(self, sample_shape=torch.Size()) -> torch.Tensor:
      return self.outputs
  
imports: from functools import partial
  from typing import Dict, Optional, Tuple, Callable
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributions import Distribution, Beta, constraints
  from gluonts.core.component import validated
  from gluonts.torch.distributions import DistributionOutput
  from gluonts.torch.modules.lambda_layer import LambdaLayer
label_desc: Use image node_id 2 for calling the ImplicitQuantileNetwork obj's underlying member method code with example usage: obj.sample(sample_shape) and returns: self